---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2_left_out_switchboard
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# gpt2_left_out_switchboard

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9378

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.983         | 0.24  | 500   | 5.0786          |
| 4.7603        | 0.48  | 1000  | 4.6865          |
| 4.4521        | 0.73  | 1500  | 4.4635          |
| 4.2512        | 0.97  | 2000  | 4.3124          |
| 4.0458        | 1.21  | 2500  | 4.2272          |
| 3.9687        | 1.45  | 3000  | 4.1443          |
| 3.9024        | 1.69  | 3500  | 4.0705          |
| 3.8439        | 1.93  | 4000  | 4.0057          |
| 3.6791        | 2.18  | 4500  | 3.9845          |
| 3.6259        | 2.42  | 5000  | 3.9471          |
| 3.6137        | 2.66  | 5500  | 3.9057          |
| 3.592         | 2.9   | 6000  | 3.8654          |
| 3.4438        | 3.14  | 6500  | 3.8758          |
| 3.3844        | 3.38  | 7000  | 3.8570          |
| 3.3977        | 3.63  | 7500  | 3.8324          |
| 3.4015        | 3.87  | 8000  | 3.8053          |
| 3.2638        | 4.11  | 8500  | 3.8300          |
| 3.1771        | 4.35  | 9000  | 3.8250          |
| 3.1914        | 4.59  | 9500  | 3.8070          |
| 3.1993        | 4.84  | 10000 | 3.7853          |
| 3.1089        | 5.08  | 10500 | 3.8146          |
| 2.9539        | 5.32  | 11000 | 3.8262          |
| 2.9853        | 5.56  | 11500 | 3.8173          |
| 2.9984        | 5.8   | 12000 | 3.8020          |
| 2.9462        | 6.04  | 12500 | 3.8259          |
| 2.7343        | 6.29  | 13000 | 3.8527          |
| 2.7724        | 6.53  | 13500 | 3.8499          |
| 2.7817        | 6.77  | 14000 | 3.8423          |
| 2.7789        | 7.01  | 14500 | 3.8510          |
| 2.5477        | 7.25  | 15000 | 3.8873          |
| 2.5643        | 7.5   | 15500 | 3.8904          |
| 2.5842        | 7.74  | 16000 | 3.8896          |
| 2.5913        | 7.98  | 16500 | 3.8858          |
| 2.4293        | 8.22  | 17000 | 3.9177          |
| 2.4253        | 8.46  | 17500 | 3.9231          |
| 2.4274        | 8.7   | 18000 | 3.9240          |
| 2.4331        | 8.95  | 18500 | 3.9254          |
| 2.362         | 9.19  | 19000 | 3.9346          |
| 2.3519        | 9.43  | 19500 | 3.9373          |
| 2.3498        | 9.67  | 20000 | 3.9378          |
| 2.3461        | 9.91  | 20500 | 3.9378          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3